Verifying 1D Heat Diffusion PDE Solutions

In summary: I think I made a mistake there. In summary, the conversation discusses the process of solving a PDE with given boundary conditions. A Fourier series is used as a "guess" for the solution, and the integration involves a constant of integration that may be a function of x or t. The conversation also addresses some concerns about the given solution and clarifies the role of the constant of integration.
  • #1
Big-T
64
0
I have trying solving this PDE for some random boundary values, and were wondering if someone could verify my calculations?

[tex]
\[
\begin{array}{l}
T_t = DT_{xx} \\
T\left( {0,t} \right) = 0,T(\pi ,t) = 0,T(x,0) = \frac{1}{4}\left( {\left( {x - \frac{\pi }{2}} \right)^2 + \frac{{\pi ^2 }}{4}} \right) \\
T = T\left( {x,t} \right) = \sum\limits_{n = 1}^\infty {a_n \left( t \right)\sin \left( {nx} \right)} \\
T_{xx} = - n^2 \sum\limits_{n = 1}^\infty {a_n \left( t \right)\sin \left( {nx} \right)} \\
T_t = \sum\limits_{n = 1}^\infty {a_n '\left( t \right)\sin \left( {nx} \right)} \\
\Rightarrow \sum\limits_{n = 1}^\infty {a_n '\left( t \right)\sin \left( {nx} \right)} = \sum\limits_{n = 1}^\infty {\left( { - n^2 a_n \left( t \right)} \right)\sin \left( {nx} \right)} \\
\Rightarrow a_n '\left( t \right) = - n^2 a_n \left( t \right) \\
\Rightarrow a_n \left( t \right) = C_n \left( x \right)e^{ - n^2 t} \\
T = \sum\limits_{n = 1}^\infty {C_n \left( x \right)e^{ - n^2 t} \sin \left( {nx} \right)} \\ \end{array}
[/tex]
[tex]
\begin{array}{l}
C_n = \frac{2}{\pi }\int\limits_0^\pi {f\left( x \right)\sin \left( {nx} \right)dx} \\
= \frac{1}{{2\pi }}\int\limits_0^\pi {\left( {x^2 - \pi x} \right)\sin \left( {nx} \right)dx} \\
= \frac{1}{{2\pi }}\left[ {\mathop {\frac{1}{{n^2 }}\sin \left( {nx} \right)\left( {2x + \pi } \right)}\limits_{ = 0} - \frac{1}{{n^3 }}\cos \left( {nx} \right)\left( {n^2 \left( {x^2 + \pi x} \right) - 1} \right)} \right]_0^\pi \\
= \frac{1}{{\pi n^3 }}\left( {1 - n^2 \pi ^2 } \right) \\
T\left( {x,t} \right) = \sum\limits_{n = 1}^\infty {\pi ^{ - 1} n^{ - 3} \left( {1 - n^2 \pi ^2 } \right)e^{ - n^2 t} \sin \left( {nx} \right)} \\
\end{array}
\]
[/tex]
 
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  • #2
Well your third initial condition is not satisfied by the answer as far as I can tell (the T(x,0)). Was the T(x,t)=a(t)*sin(nx) given or is that something you came up with?

The other thing that's sort of bothering me is in line 8. The integration really isn't dealing with anything relating to an f(x) there as far as I can tell so my question is why is it C(x) rather than C(t)?

The rest looks ok to me.
 
  • #3
The reason I'm asking is that this is the first time I have attempted to solve such an equation, I have not taken any courses dealing with this subject.

The T(x,t) is a Fourier series "guess" for a solution of the equation, this is allowed because Fourier's theorem says that every function may be represented by an infinite number of harmonics, (i.e. sin(pi*n*x/L)).

I think it is C(x) because when integration a_n(t), the constant of integration is actually a function of x, since we are dealing with partial derivation.
 
  • #4
Well the Fourier series would be of the form found here http://en.wikipedia.org/wiki/Fourier_series and would involve a cos() term as well.

I don't think there is any specific reason that the constant should be a function of x, it could just be a constant.
 
  • #5
Yes, it would have involved some cos() terms, but with these spesific boundary conditions, all the cosines vanish, and only the sines are left behind.

As for the constant of integration, you're probably right.
 

1. What is a 1D heat diffusion PDE?

A 1D heat diffusion PDE (partial differential equation) is a mathematical equation that describes the flow of heat in one dimension over time. It takes into account factors such as the initial temperature distribution, thermal conductivity, and boundary conditions to model the change in temperature at different points along a one-dimensional spatial domain.

2. Why is it important to verify solutions to 1D heat diffusion PDEs?

Verifying solutions to 1D heat diffusion PDEs is important to ensure the accuracy and validity of the results. It allows us to check for errors in the mathematical model, numerical approximations, and boundary conditions. Additionally, verifying solutions helps to validate the predictions made by the model and provides confidence in using it for further analysis or real-world applications.

3. What methods can be used to verify solutions to 1D heat diffusion PDEs?

There are several methods that can be used to verify solutions to 1D heat diffusion PDEs, including analytical methods, numerical methods, and experimental methods. Analytical methods involve using mathematical techniques to derive an exact solution to the PDE, which can then be compared to the numerical solution. Numerical methods use computer algorithms to approximate the solution and can be compared to other numerical methods or experimental data. Experimental methods involve conducting physical experiments to measure the temperature distribution and comparing it to the predicted solution.

4. How can boundary conditions affect the accuracy of 1D heat diffusion PDE solutions?

Boundary conditions play a crucial role in determining the accuracy of 1D heat diffusion PDE solutions. If the boundary conditions are not correctly specified or are unrealistic, it can lead to incorrect solutions. For example, if the boundary conditions do not accurately reflect the physical system being modeled, the predicted temperature distribution will not match the actual temperature distribution, resulting in inaccurate solutions.

5. What are some common sources of error when verifying solutions to 1D heat diffusion PDEs?

Some common sources of error when verifying solutions to 1D heat diffusion PDEs include mathematical errors, numerical errors, and experimental errors. Mathematical errors can occur when solving the PDE using analytical methods, such as making mistakes in the calculations or assumptions. Numerical errors can arise from using approximate methods or insufficiently fine grids. Experimental errors can occur due to measurement errors or uncertainties in the experimental setup.

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